Boosting functional response models for location, scale and shape with an application to bacterial competition
| Autor: | Sarah Brockhaus, Sonja Greven, Madeleine Opitz, Sophia Anna Schaffer, Benedikt von Bronk, Almond Stöcker |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Boosting (machine learning) functional regression Generalized additive model Functional response Distributional regression 510 Mathematik bacterial growth Statistics - Applications Regression Methodology (stat.ME) GAMLSS Applications (stat.AP) Functional regression ddc:610 ddc:510 Statistics Probability and Uncertainty 610 Medizin und Gesundheit Biological system Statistics - Methodology functional data Mathematics |
| Zdroj: | Statistical Modelling. 21:385-404 |
| ISSN: | 1477-0342 1471-082X |
| Popis: | We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves, variances and other distributional parameters of the response in dependence of various scalar and functional covariate effects. In addition, the scope of distributions is extended beyond exponential families. The model is fitted via gradient boosting, which offers inherent model selection and is shown to be suitable for both complex model structures and highly auto-correlated response curves. This enables us to analyze bacterial growth in \textit{Escherichia coli} in a complex interaction scenario, fruitfully extending usual growth models. Comment: bootstrap confidence interval type uncertainty bounds added; minor changes in formulations |
| Databáze: | OpenAIRE |
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